Ernest Chan | Principal
QTS Capital Management

Ernest Chan, Principal, QTS Capital Management

Dr. Ernest P. Chan is the Managing Member of QTS Capital Management, LLC. His career since 1994 has been focusing on the development of statistical models and advanced computer algorithms to find patterns and trends in large quantities of data. He has applied his expertise in statistical pattern recognition to projects ranging from textual retrieval at IBM Research, mining customer relationship data at Morgan Stanley, and statistical arbitrage trading strategy research at Credit Suisse, Mapleridge Capital Management, and other hedge funds.

While at the Human Language Technologies group at IBM T. J. Watson Research Center (Yorktown Heights, NY), Dr. Chan spearheaded IBM’s research effort to develop a system for searching large text databases such as the World Wide Web, catapulting IBM’s reputation as a top player in the field. His system was placed seventh among some forty competitors in a competition sponsored by the National Institute of Science and Technology and the Department of Defense in 1996. At the Artificial Intelligence and Data Mining group in Morgan Stanley’s headquarter in New York, Ernie pioneered the application of some of these sophisticated statistical algorithms to the complex task of extracting customer relationships in the Morgan Stanley customer accounts database.

Ernie was invited to join a proprietary trading group at Credit Suisse in New York in 1998 to develop statistical models for equities and futures trading. He later joined Mapleridge Capital Management Corp. in 2002 as a Senior Quantitative Analyst working on futures trading strategies, and then Maple Financial in 2003 as a senior researcher and trader.

Ernie writes the Quantitative Trading blog and was quoted by the New York Times, Forbes, and the CIO magazine, and interviewed on CNBC’s Closing Bell program, Technical Analysis of Stocks and Commodities magazine, Securities Industry News, Automated Trader magazine, and the CFA Institute Magazine on topics related to quantitative trading. In recognition of his expertise in statistical data mining, he was invited to serve on the Program Committees of the International Conference of Knowledge Discovery and Data Mining in 1998. He was an invited speaker at the Automated Trading conference in London, UK, in October 2009, the Market Technicians Association Toronto Annual Conference in 2010, the Quant Invest Canada conference in 2012, QuantCon in New York in 2015-17 and as a keynote speaker in 2018. He is the author of “Quantitative Trading: How to Build Your Own Algorithmic Trading Business” and “Algorithmic Trading: Winning Strategies and Their Rationale“, both published by John Wiley & Sons. His new book “Machine Trading: Deploying Computer Algorithms to Conquer the Markets” was published in 2017. Ernie conducts workshops on Statistical Arbitrage, Quantitative Momentum Strategies, and Artificial Intelligence for Traders in London. He was an Adjunct Associate Professor of Finance at Nanyang Technological University in Singapore, and an Industry Fellow of the NTU-SGX Centre for Financial Education, which is jointly set up by NTU and the Singapore Exchange. He is on the faculty of Northwestern University’s Master of Advanced Data Science program and supervises student theses there.

Ernie holds a Bachelor of Science degree from University of Toronto in 1988, a Master of Science (1991) and a Doctor of Philosophy (1994) degree in theoretical physics from Cornell University.


Quant World Canada @ 11:00

Data sweet spot – how are you finding, evaluating and applying ‘edgeworthy' alternative data sources?

  • Investing in the right datasets – how do you evaluate which data sets are worth adopting in the face of high fees and uncertain value?
  • IT & talent requirements for value extraction – what technology and human capital capabilities do you need in place to effectively analyze the data and realize its value within a reasonable time horizon?
  • Comparing source, speed & edge – real-time market data, historical market data, macroeconomic data, public/private company data, alternative data
  • Alternative data, alternative use cases – what other applications may exist for alt data sets outside of alpha generation?
  • Other nascent data applications – do you see any immediate or future value for trading firms and fund managers in data from nanosatellites, drone imagery and Internet of Things?

Quant World Canada @ 12:20


AI & market analysis – leveraging machine-driven insights to guide investment decision making

Quant World Canada @ 14:50

Meta-Labelling: A key financial machine learning tool

  • An overview and analysis of what Meta-labelling is and how you can apply it
  • Meta-labelling and "quantamental" strategies
  • Applying Meta-labelling for asset allocation and risk management
last published: 18/Dec/18 18:15 GMT

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